This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Identification and quantification of microorganisms is an important step in studying the alpha and beta diversities within and between microbial communities respectively. Both, identification and quantification of a given microbial community can be carried out using whole genome shotgun sequences with less bias than using 16S-rRNA sequences. However, shared regions of DNA among reference genomes and taxonomic units pose a significant challenge in assigning reads correctly to their true origins. The existing microbial community profiling tools commonly deal with this problem by either preparing signature-based unique references or assigning an ambiguous read to its least common ancestor in a taxonomic tree. The former method is limited to making use of the reads which can be mapped to the curated regions, while the later suffer from the lack of uniquely-mapping reads at higher (more specific) taxonomic ranks. Moreover, even if the tools exhibited generally good performance in calling the organisms present in a sample, there is room for improvement in calling the correct relative abundance of the organisms. We present a new method Species Level Identification of Microorganisms from Metagenomes (SLIMM) which addresses the above issues by using coverage information of reference genomes to remove unlikely genomes from the analysis and subsequently gain more uniquely-mapping reads to assign at higher ranks of a taxonomic tree. SLIMM is based on a few, seemingly easy steps which lead to a tool that outperforms state-of-the-art tools in run-time and/or memory usage while being on par or better in computing quantitative and qualitative information at the species level.
This submission is intended for the GCB2016 Conference Collection.
Details of datasets used for the study
Accuracy comparison of different methods per datasets
Runtime for each dataset
Statistical details (STDDEV, MEAN, Variance, Q1, Q2(median), Q3 ) of the difference b/n real and predicted abundance